Identifying User Goals from Web Search Results

Chang, Yao-Sheng; He, Kuan-Yu; Yu, Scott; Lu, Wen-Hsiang

doi:10.1109/wi.2006.93

Cited by 13 publications

(6 citation statements)

References 2 publications

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“…Earlier work on understanding search behavior focused on methods for classifying queries into high-level search goals, such as informational, navigational and transactional [8][18] [29]. Downey et al [10] studied the relationship between information needs and their formulation as search queries.…”

Section: Related Workmentioning

confidence: 99%

Modeling and analysis of cross-session search tasks

Kotov

Bennett

White

et al. 2011

Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval

120

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The information needs of search engine users vary in complexity, depending on the task they are trying to accomplish. Some simple needs can be satisfied with a single query, whereas others require a series of queries over a longer period of time. While search engines effectively satisfy many simple needs, searchers receive little support when their information needs span sessions. In this work, we propose methods for modeling and analyzing user search behavior that extends over multiple search sessions. We focus on two problems: (i) given a user query, identify all related queries from previous sessions that the user has issued, and (ii) given a multi-query task for a user, predict whether the user will return to this task in the future. We model both problems within a classification framework that uses features of individual queries and long-term user search behavior at different granularity. Experimental evaluation of the proposed models for both tasks indicates that it is possible to effectively model and analyze crosssession search behavior. Our findings have implications for improving search for complex information needs and designing search engine features to support cross-session search tasks.

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Section: Related Workmentioning

confidence: 99%

Modeling and analysis of cross-session search tasks

Kotov

Bennett

White

et al. 2011

Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval

120

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“…While there is a significant body of research on understanding user intent during search ( [6], [23], [13], [5], [18], [10], [7]), to the best of our knowledge, the application of user intent to query suggestion is a novel idea which has not been studied yet. In this paper, we introduce and define the concept of Intentional Query Suggestion and present a prototypical algorithm as first evidence for the feasibility of this idea.…”

Section: Discussionmentioning

confidence: 99%

Intentional query suggestion

Strohmaier

Kroll

Körner

2009

Proceedings of the 2009 Workshop on Web Search Click Data

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The degree to which users' make their search intent explicit can be assumed to represent an upper bound on the level of service that search engines can provide. In a departure from traditional query expansion mechanisms, we introduce Intentional Query Suggestion as a novel idea that is attempting to make users' intent more explicit during search. In this paper, we present a prototypical algorithm for Intentional Query Suggestion and we discuss corresponding data from comparative experiments with traditional query suggestion mechanisms. Our preliminary results indicate that intentional query suggestions 1) diversify search result sets (i.e. it reduces result set overlap) and 2) have the potential to yield higher click-through rates than traditional query suggestions.

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“…Y. Chang, He, Yu, and Lu (2006) analyzed user goals from the viewpoint of natural language processing. They assumed that the subject of the hidden sentence in the user's mind was the user himself and that the combined pair of the verb and object was called "VOpair."…”

Section: Related Workmentioning

confidence: 99%

Detecting temporal patterns of user queries

Ren¹,

Chen²,

Ma³

et al. 2015

Asso for Info Science & Tech

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Query classification is an important part of exploring the characteristics of web queries. Existing studies are mainly based on Broder's classification scheme and classify user queries into navigational, informational, and transactional categories according to users' information needs. In this article, we present a novel classification scheme from the perspective of queries' temporal patterns. Queries' temporal patterns are inherent time series patterns of the search volumes of queries that reflect the evolution of the popularity of a query over time. By analyzing the temporal patterns of queries, search engines can more deeply understand the users' search intents and thus improve performance. Furthermore, we extract three groups of features based on the queries' search volume time series and use a support vector machine (SVM) to automatically detect the temporal patterns of user queries. Extensive experiments on the Million Query Track data sets of the Text REtrieval Conference (TREC) demonstrate the effectiveness of our approach.

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Identifying User Goals from Web Search Results

Cited by 13 publications

References 2 publications

Modeling and analysis of cross-session search tasks

Modeling and analysis of cross-session search tasks

Intentional query suggestion

Detecting temporal patterns of user queries

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